The Experts below are selected from a list of 1898529 Experts worldwide ranked by ideXlab platform
Stephen M Fiore - One of the best experts on this subject based on the ideXlab platform.
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social cognitive and affective neuroscience in human machine Systems a roadmap for improving training human robot interaction and team performance
IEEE Transactions on Human-Machine Systems, 2014Co-Authors: Travis J Wiltshire, Stephen M FioreAbstract:This paper augments recent advances in social cognitive and affective neuroscience (SCAN) and illustrates their relevance to the development of novel human–machine Systems. Advances in this area are crucial for understanding and exploring the social, cognitive, and neural processes that arise during human interactions with complex sociotechnological Systems. Overviews of the major areas of SCAN research, including emotion, theory of mind, and joint action, are provided as the basis for describing three applications of SCAN to human–machine Systems research and development. Specifically, this paper provides three examples to demonstrate the broad interdisciplinary applicability of SCAN and the ways it can contribute to improving a number of human–machine Systems with the pursuit of further research in this vein. These include applying SCAN to learning and training, informing the field of human–robot interaction (HRI), and, finally, for enhancing team performance. The goal is to draw attention to the insights that can be gained by integrating SCAN with ongoing human–machine System research and to provide guidance to foster collaborations of this nature. Toward this end, we provide a Systematic set of notional research questions for each detailed application within the context of the three major emphases of SCAN research. In turn, this study serves as a roadmap for preliminary investigations that integrate SCAN and human–machine System research.
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Social Cognitive and Affective Neuroscience in Human–Machine Systems: A Roadmap for Improving Training, Human–Robot Interaction, and Team Performance
IEEE Transactions on Human-Machine Systems, 2014Co-Authors: Travis J Wiltshire, Stephen M FioreAbstract:This paper augments recent advances in social cognitive and affective neuroscience (SCAN) and illustrates their relevance to the development of novel Human-Machine Systems. Advances in this area are crucial for understanding and exploring the social, cognitive, and neural processes that arise during human interactions with complex sociotechnological Systems. Overviews of the major areas of SCAN research, including emotion, theory of mind, and joint action, are provided as the basis for describing three applications of SCAN to Human-Machine Systems research and development. Specifically, this paper provides three examples to demonstrate the broad interdisciplinary applicability of SCAN and the ways it can contribute to improving a number of Human-Machine Systems with the pursuit of further research in this vein. These include applying SCAN to learning and training, informing the field of human-robot interaction (HRI), and, finally, for enhancing team performance. The goal is to draw attention to the insights that can be gained by integrating SCAN with ongoing Human-Machine System research and to provide guidance to foster collaborations of this nature. Toward this end, we provide a Systematic set of notional research questions for each detailed application within the context of the three major emphases of SCAN research. In turn, this study serves as a roadmap for preliminary investigations that integrate SCAN and Human-Machine System research.
Travis J Wiltshire - One of the best experts on this subject based on the ideXlab platform.
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social cognitive and affective neuroscience in human machine Systems a roadmap for improving training human robot interaction and team performance
IEEE Transactions on Human-Machine Systems, 2014Co-Authors: Travis J Wiltshire, Stephen M FioreAbstract:This paper augments recent advances in social cognitive and affective neuroscience (SCAN) and illustrates their relevance to the development of novel human–machine Systems. Advances in this area are crucial for understanding and exploring the social, cognitive, and neural processes that arise during human interactions with complex sociotechnological Systems. Overviews of the major areas of SCAN research, including emotion, theory of mind, and joint action, are provided as the basis for describing three applications of SCAN to human–machine Systems research and development. Specifically, this paper provides three examples to demonstrate the broad interdisciplinary applicability of SCAN and the ways it can contribute to improving a number of human–machine Systems with the pursuit of further research in this vein. These include applying SCAN to learning and training, informing the field of human–robot interaction (HRI), and, finally, for enhancing team performance. The goal is to draw attention to the insights that can be gained by integrating SCAN with ongoing human–machine System research and to provide guidance to foster collaborations of this nature. Toward this end, we provide a Systematic set of notional research questions for each detailed application within the context of the three major emphases of SCAN research. In turn, this study serves as a roadmap for preliminary investigations that integrate SCAN and human–machine System research.
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Social Cognitive and Affective Neuroscience in Human–Machine Systems: A Roadmap for Improving Training, Human–Robot Interaction, and Team Performance
IEEE Transactions on Human-Machine Systems, 2014Co-Authors: Travis J Wiltshire, Stephen M FioreAbstract:This paper augments recent advances in social cognitive and affective neuroscience (SCAN) and illustrates their relevance to the development of novel Human-Machine Systems. Advances in this area are crucial for understanding and exploring the social, cognitive, and neural processes that arise during human interactions with complex sociotechnological Systems. Overviews of the major areas of SCAN research, including emotion, theory of mind, and joint action, are provided as the basis for describing three applications of SCAN to Human-Machine Systems research and development. Specifically, this paper provides three examples to demonstrate the broad interdisciplinary applicability of SCAN and the ways it can contribute to improving a number of Human-Machine Systems with the pursuit of further research in this vein. These include applying SCAN to learning and training, informing the field of human-robot interaction (HRI), and, finally, for enhancing team performance. The goal is to draw attention to the insights that can be gained by integrating SCAN with ongoing Human-Machine System research and to provide guidance to foster collaborations of this nature. Toward this end, we provide a Systematic set of notional research questions for each detailed application within the context of the three major emphases of SCAN research. In turn, this study serves as a roadmap for preliminary investigations that integrate SCAN and Human-Machine System research.
Sanjeev Sharma - One of the best experts on this subject based on the ideXlab platform.
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An exploratory study of chaos in Human-Machine System dynamics
IEEE Transactions on Systems Man and Cybernetics Part A:Systems and Humans, 2006Co-Authors: Sanjeev SharmaAbstract:The Human-Machine System behavior and performance are dynamic, nonlinear, and possibly chaotic. Various techniques have been used to describe such dynamic and nonlinear System characteristics. However, these techniques have rarely been able to accommodate the chaotic behavior of such a nonlinear System. Therefore, this study proposes the use of nonlinear dynamic System theory as one possible technique to account for the dynamic, nonlinear, and possibly chaotic Human-Machine System characteristics. It briefly describes some of the available nonlinear dynamic System techniques and illustrates how their application can explain various properties of the Human-Machine System. A pilot's heart interbeat interval (IBI) and altitude tracking error time series data are used in the illustration. Further, the possible applications of the theory in various domains of human factors for on-line assessment, short-term prediction, and control of Human-Machine System behavior and performance are discussed.
Frédéric Vanderhaegen - One of the best experts on this subject based on the ideXlab platform.
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Can dissonance engineering improve risk analysis of human---machine Systems?
Cognition Technology & Work, 2017Co-Authors: Frédéric Vanderhaegen, Oliver CarstenAbstract:The paper discusses dissonance engineering and its application to risk analysis of human---machine Systems. Dissonance engineering relates to sciences and technologies relevant to dissonances, defined as conflicts between knowledge. The richness of the concept of dissonance is illustrated by a taxonomy that covers a variety of cognitive and organisational dissonances based on different conflict modes and baselines of their analysis. Knowledge control is discussed and related to strategies for accepting or rejecting dissonances. This acceptability process can be justified by a risk analysis of dissonances which takes into account their positive and negative impacts and several assessment criteria. A risk analysis method is presented and discussed along with practical examples of application. The paper then provides key points to motivate the development of risk analysis methods dedicated to dissonances in order to identify the balance between the positive and negative impacts and to improve the design and use of future human---machine System by reinforcing knowledge.
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Can dissonance engineering improve risk analysis of human–machine Systems?
Cognition Technology & Work, 2017Co-Authors: Frédéric Vanderhaegen, Oliver CarstenAbstract:The paper discusses dissonance engineering and its application to risk analysis of human–machine Systems. Dissonance engineering relates to sciences and technologies relevant to dissonances, defined as conflicts between knowledge. The richness of the concept of dissonance is illustrated by a taxonomy that covers a variety of cognitive and organisational dissonances based on different conflict modes and baselines of their analysis. Knowledge control is discussed and related to strategies for accepting or rejecting dissonances. This acceptability process can be justified by a risk analysis of dissonances which takes into account their positive and negative impacts and several assessment criteria. A risk analysis method is presented and discussed along with practical examples of application. The paper then provides key points to motivate the development of risk analysis methods dedicated to dissonances in order to identify the balance between the positive and negative impacts and to improve the design and use of future human–machine System by reinforcing knowledge.
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How to learn from the resilience of Human-Machine Systems?
Engineering Applications of Artificial Intelligence, 2013Co-Authors: Kiswendsida Abel Ouedraogo, Simon Enjalbert, Frédéric VanderhaegenAbstract:This paper proposes a functional architecture to learn from resilience. First, it defines the concept of resilience applied to Human-Machine System (HMS) in terms of safety management for perturbations and proposes some indicators to assess this resilience. Local and global indicators for evaluating Human-Machine resilience are used for several criteria. A multi-criteria resilience approach is then developed in order to monitor the evolution of local and global resilience. The resilience indicators are the possible inputs of a learning System that is capable of producing several outputs, such as predictions of the possible evolutions of the System's resilience and possible alternatives for human operators to control resilience. Our System has a feedback-feedforward architecture and is capable of learning from the resilience indicators. A practical example is explained in detail to illustrate the feasibility of such prediction.
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Using adjustable autonomy and Human-Machine cooperation to make a Human-Machine System resilient - Application to a ground robotic System
Information Sciences, 2011Co-Authors: Stéphane Zieba, Philippe Polet, Frédéric VanderhaegenAbstract:This study concerns autonomous ground vehicles performing missions of observation or surveillance. These missions are accomplished under the supervision of human operators, who can also remotely control the unmanned vehicle. This kind of Human-Machine System is likely to face perturbations in a dynamic natural environment. However, human operators are not able to manage perturbations due to overload. The objective of this study is to provide such Systems with ways to anticipate, react and recover from perturbations. In other words, these works aim at improving System resilience so that it can better manage perturbations. This paper presents a model of human-robot cooperative control that helps to improve the resilience of the Human-Machine System by making the level of autonomy adjustable. A formalism of agent autonomy is proposed according to the semantic aspects of autonomy and the agent's activity levels. This formalism is then used to describe the activity levels of the global Human-Machine System. Hierarchical decision-making methods and planning algorithms are also proposed to implement these levels of activity. Finally, an experimental illustration on a micro-world is presented in order to evaluate the feasibility and application of the proposed model.
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Human factors in studies of the safety and reliability of agro-equipment
2010Co-Authors: W. Ben Yahia, Frédéric Vanderhaegen, P. Polet, N. TricotAbstract:This paper aims to show the importance of taking into account human factors in risk analysis. Safety analysis methods identify System failures and dangers, thus allowing risk to be analyzed. However, although erroneous human behaviour can affect the Human-Machine System (HMS), studies using safety analysis usually do not explicitly take the human factors into account. In fact, human operators constitute an important element for System safety since they simultaneously represents a System user and a System component. They manage and recuperate degraded System states, especially during unforeseen and/or unknown events. It is thus important to integrate human factors into safety analysis. This observation led to the development of human behaviour models and human reliability analysis methods. This paper presents the relevance of human reliability analysis methods for dealing with problems in the domain of agriculture and examines whether or not these methods can be applied to this domain.
Shigeyasu Kawaji - One of the best experts on this subject based on the ideXlab platform.
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Experimental study of collaborater in human–machine System
Mechatronics, 2009Co-Authors: Hirofumi Ohtsuka, Koki Shibasato, Shigeyasu KawajiAbstract:Abstract A human intervention is essential for human–machine System and human operator’s skill affects deeply achievement of the control purpose. By keeping the human operation easy, an operator can concentrate on more advanced decision so that the operation performance is improved. In this paper, a new concept of human-oriented compensator is proposed for improving the human–machine System, which is named “collaborater”. The design approach exists in human dynamics, and 2DOF structure is introduced. The simulation results confirm that the time and frequency responses are improved. Moreover the experimental results also confirm the similar effect of proposed method.
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Adaptive collaborative compensator design method for Human-Machine System
2008 International Conference on Control Automation and Systems ICCAS 2008, 2008Co-Authors: Hirofumi Ohtsuka, Koki Shibasato, Shigeyasu KawajiAbstract:The control performance of manual controlled Human-Machine System considerably depends on the skill level of human operator. So, authors have proposed a concept of ldquoCollaboraterrdquo which coexists with human operator as a parallel control element and can collaboratively control the machine in order to modify the response of Human-Machine System. In this report, for the variety of human dynamics, an adaptive collaborater using Neural Network is considered.
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EXPERIMENTAL STUDY OF COLLABORATER IN Human-Machine System
IFAC Proceedings Volumes, 2006Co-Authors: Hirofumi Ohtsuka, Koki Shibasato, Shigeyasu KawajiAbstract:Abstract A human intervention is essential for Human-Machine System and human operator's skill affects deeply achievement of the control purpose. By keeping the human operation easy, an operator can concentrate on more advanced decision so that the operation performance is improved. In this paper, a new concept of human-oriented compensator is proposed for improving the Human-Machine System, which is named “collaborater”. The design approach exists in human dynamics, and 2DOF structure is introduced. The simulation results confirm that the time and frequency responses are improved. Moreover the experimental results also confirm the similar effect of proposed method.
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CONCEPT, STRUCTURE AND DESIGN OF “COLLABORATER” IN Human-Machine System
IFAC Proceedings Volumes, 2005Co-Authors: Koki Shibasato, Hirofumi Ohtsuka, Shigeyasu KawajiAbstract:Abstract A human intervention is essential for Human-Machine System and human operator's skill affects deeply achievement of the control purpose. By keeping the human operation easy, an operator can concentrate on more advanced decision so that the operation performance is improved. In this paper, a new concept of human-oriented compensator is proposed for improving the Human-Machine System, which is named “collaborater”. The design approach exists in human dynamics, and 2DOF structure is introduced. The simulation results confirm that the time and frequency responses are improved. Moreover an adaptive function against changes of human dynamics is constructed using neural networks.
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Human-oriented compensator for Human-Machine System
2004Co-Authors: Koki Shibasato, Hirofumi Ohtsuka, H. Uemura, Shigeyasu KawajiAbstract:In a Human-Machine System, the operator's skill is required considerably in order to realize a meaningful operation. In this paper, a new concept of human-oriented compensator is proposed for improving the Human-Machine System. That is called "collaborater". The collaborater aims to support a human work and not to obstruct the operational feeling. The design strategy exists in the control structure of the biological feedback. Two degree of freedom compensator enables the improvement of the control performance. When collaborater is applied to a Human-Machine System, it is conjectured that the effect of collaborater decreases due to the adaptation of the human operator to the mechanical System with collaborater. An adaptive collaborater against a change of parameters is proposed by the estimation of human dynamics using the neural network.
